‘Hypernudge’: Big Data as a mode of regulation by design

Abstract

words max)This paper draws on regulatory governance scholarship to argue that the analytic phenomenon currently known as 'Big Data' can be understood as a mode of 'design-based' regulation. Although Big Data decision-making technologies can take the form of automated decision-making systems, this paper focuses on algorithmic decision-guidance techniques. By highlighting correlations between data items that would not otherwise be observable, these techniques are being used to shape the informational choice context in which individual decision-making occurs, with the aim of channelling attention and decision-making in directions preferred by the 'choice architect'. By relying upon the use of 'nudge'-a particular form of choice architecture that alters people's behaviour in a predictable way without forbidding any options or significantly changing their economic incentives, these techniques constitute a 'soft' form of design-based control. But, unlike the static Nudges popularised by Thaler and Sunstein (2008) such as placing the salad in front of the lasagne to encourage healthy eating, Big Data analytics nudges are extremely powerful and potent due to their networked, continuously updated, dynamic and pervasive nature (hence 'hypernudge'). I adopt a liberal, rights-based critique of these techniques, contrasting liberal theoretical accounts with selective insights from science and technology studies (STS) and surveillance studies on the other. I argue that concerns about the legitimacy of these techniques are not satisfactorily resolved through reliance on individual notice and consent, touching upon the troubling implications for democracy and human flourishing if Big Data analytic techniques driven by commercial self-interest continue their onward march unchecked by effective and legitimate constraints.

A Maussian bargain: Accumulation by gift in the digital economy

“…Meanwhile, digital firms use a variety of psychological, emotional, and economic incentives, disincentives, and heuristics to repeatedly nudge users and customers to "share" ever more information (Acquisti et al, 2015;Fourcade, 2017;Yeung, 2017). Thaler and Sunstein (2008) use the term "choice architecture" to refer to these techniques.…”

Mass personalization: Predictive marketing algorithms and the reshaping of consumer knowledge

“…As noted by MacKenzie (2018), the central issue in recent debates about Big Data, privacy, commercial and political targeting, filter bubbles and so on, is how individual can or should be taken into account in the calculation, and with what consequences on society. Personalization simultaneously represents a promise of emancipation from the broad statistical categories of private and public bureaucracies (Cardon, 2015;Th evenot, 2019), and a potential threat to our privacy and freedom of choice, as it often implies surveillance, targeting and nudging (Lyon, 2014;Yeung, 2017).…”

Ethical Implications and Accountability of Algorithms

“…Which features of the data set are selected as important may be either appropriate or inappropriate for the decision at hand. Attorney General Eric Holder perhaps best summarizes this concern in regard to risk assessment algorithms in criminal justice: I am concerned that they [algorithms used in sentencing] inadvertently undermine our efforts to ensure individualized and equal justice, … Criminal sentences 3 Similarly, algorithms act like design-based regulation (Yeung 2017) where algorithms can be used for the consistent application of legal and regulatory regimes (Thornton 2016(Thornton , p. 1826; algorithms can enforce morality (Diakopoulos 2013)-while still being designed and used by individuals. 4 For algorithms, in addition to directly coding the algorithm to prioritize one group more than any others, two mechanisms can also indirectly drive bias in the process: proxies and machine learning.…”